ArticleJournal of clinical laboratory analysis2021
Establishment of clinical diagnostic models using glucose, lipid, and urinary polypeptides in gestational diabetes mellitus.
Article in Journal of clinical laboratory analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 6 citations in OpenAlex.
- First-trimester biomarkers of gestational diabetes mellitus: A scoping review.Acta obstetricia et gynecologica Scandinavica · 2025Article
- Artificial Intelligence in Gestational Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2025Review
- Proteomic analysis of plasma total exosomes and placenta-derived exosomes in patients with gestational diabetes mellitus in the first and second trimesters.BMC pregnancy and childbirth · 2024Article
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Authors and funding
2 authors at 1 institution in 1 country.
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Abstract
backgroundGestational diabetes mellitus (GDM) has many adverse outcomes that seriously threaten the short-term and long-term health of mothers and infants. This study comprehensively analyzed the clinical diagnostic value of GDM-related clinical indexes and urine polypeptide research results, and established comprehensive index diagnostic models.
methodsIn this study, diagnostic values from the clinical indexes of serum triglyceride (TRIG), high-density lipoprotein cholesterol (HDL-C), fasting plasma glucose (FPG) and glycosylated hemoglobin (HbA1c), and 7 GDM-related urinary polypeptides were analyzed retrospectively. The multiple logistic regression equation, multilayer perceptron neural network model, radial basis function, and discriminant analysis function models of GDM-related indexes were established using machine language.
resultsThe results showed that HbA1c had the highest diagnostic value for GDM, with an area under the curve (AUC) of 0.769. When the cut-off value was 4.95, the diagnostic sensitivity and specificity were 70.5% and 70.0%, respectively. Among the seven GDM-related urinary polypeptides, human hemopexin (HEMO) had the highest diagnostic value, with an AUC of 0.690. When the cut-off value was 368.5, the sensitivity and specificity were 79.5% and 43.3%, respectively. The AUC of the multilayer perceptron neural network model was 0.942, followed by binary logistic regression (0.938), radial basis function model (0.909), and the discriminant analysis function model (0.908).
conclusionThe establishment of a GDM diagnostic model combining blood glucose, blood lipid, and urine polypeptide indexes can lay a foundation for exploring machine language and artificial intelligence in diagnostic systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.